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3 Rods, another type of photoreceptor cell, are only useful in low-light environments to provide what is commonly called night vision 4
click the button The PleaseWaitViewController s view is displayed and the progress view updates (Figure 11-11) Upon completion, the application removes the view and displays the original view once again Listing 11-3 PleaseWaitViewControllerh
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Thus, the naive Bayes classifier assigns the target value PlayTennis = no to this new instance, based on the probability estimates learned from the training data Furthermore, by normalizing the above quantities to sum to one we can calculate the conditional probability that the target value is no, given the observed attribute values For the current example, this probability is = -795
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You may have learned that the primary colors are red, yellow, and blue Technically, these are magenta, yellow, and cyan and usually refer to pigments rather than colors A magenta ink absorbs green light, thus controlling the amount of green color perceived by the eye Since white light is composed of equal amounts of all three colors, removing green leaves red and blue, which together form magenta Likewise, yellow ink absorbs blue light, and cyan ink absorbs red light Reflected light, such as that from a painting, is formed from the character of the illuminating light and the absorption of the pigments Projected light, such as that from a television, is formed from the intensities of the three primary colors Since video is projected, it deals with red, green, and blue colors
#import <UIKit/UIKith> @interface PleaseWaitViewController : UIViewController { IBOutlet UIProgressView * myProgress; } @property (nonatomic, retain) IBOutlet UIProgressView * myProgress; @end
The use of Y for luminance comes from the XYZ color system defined by the Commission Internationale de L Eclairage (CIE) The system uses three-dimensional space to represent colors, where the Y axis is luminance and X and Z axes represent color information
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(continued)
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6 Luminance from RGB can be a difficult concept to grasp It may help to think of colored filters If you look through a red filter, you will see a monochromatic image composed of shades of red The image would look the same through the red filter if it were changed to a different color, such as gray Since the red filter only passes red light, anything that is pure blue or pure green will not be visible To get a balanced image, you would use three filters, change the image from each one to gray, and average them together
Figure 11-10
Up to this point we have estimated probabilities by the fraction of times the event is observed to occur over the total number of opportunities For example, in the above case we estimated P(Wind = strong]Play Tennis = no) by the fraction % where n = 5 is the total number of training examples for which PlayTennis = no, and n, = 3 is the number of these for which Wind = strong While this observed fraction provides a good estimate of the probability in many cases, it provides poor estimates when n, is very small To see the difficulty, imagine that, in fact, the value of P(Wind = strongl PlayTennis = no) is 08 and that we have a sample containing only 5 examples for which PlayTennis = no Then the most probable value for n, is 0 This raises two difficulties First, $ produces a biased underestimate of the probability Second, when this probability estimate is zero, this probability term will dominate the Bayes classifier if the future query contains Wind = strong The reason is that the quantity calculated in Equation (620) requires multiplying all the other probability terms by this zero value To avoid this difficulty we can adopt a Bayesian approach to estimating the probability, using the m-estimate defined as follows
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